Digital Twin of Fused Filament Fabrication Prints for Finite Element Analysis via G-Code Reverse Engineering

制作 有限元法 熔丝制造 逆向工程 蛋白质丝 材料科学 编码(集合论) 工程制图 结构工程 机械工程 计算机科学 工程类 复合材料 程序设计语言 3D打印 集合(抽象数据类型) 替代医学 病理 医学
作者
S. Ochoa,Santiago Ferrándiz Bou,Luis Garzón,Christian Cobos
出处
期刊:3D printing and additive manufacturing [Mary Ann Liebert, Inc.]
卷期号:12 (6): 611-620 被引量:4
标识
DOI:10.1089/3dp.2023.0325
摘要

As additive manufacturing by fused filament fabrication has gained popularity, computational analysis has become fundamental in predicting the mechanical behavior of 3D models. This paper proposes the development of a method for the finite element (FE) simulation of 3D-printed parts, implementing model design reverse engineering using G-code to obtain their digital twins (DTs). Samples were printed under the ASTM D638 standard with different nozzle diameters and layer heights, which allowed them to be mechanically characterized by tensile tests. The tensile tests determined that the diameter of the nozzles used (between 0.2 mm and 1.0 mm) influences the material’s tensile strength. The greater the diameter, the greater the stiffness, which translates into a change in the Young’s modulus, as well as greater tensile strength and thus a reduction of the deformation, for which a value of 2.66 ± 0.6 % was obtained, i.e., the filament diameter did not influence this aspect. After carrying out the reverse engineering process of the samples to obtain DTs of the physical models, the printing G-code was used with the help of a Python script for their conversion to trajectories. These trajectories were introduced into Rhinoceros software with the Grasshopper add-on to obtain the reconstructed 3D models. The deposited filament profile used to reconstruct the DT was obtained by microscopy of the section of the physical samples. The predominant profile observed was that of a flattened oval. FE simulation was then carried out, obtaining a similarity of 90% between the simulated and mechanical tests, which validated the proposed method of predicting mechanical stresses in printed 3D elements.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
咩eteor完成签到,获得积分20
1秒前
owenty123发布了新的文献求助20
1秒前
LYing发布了新的文献求助10
2秒前
ym完成签到,获得积分10
2秒前
2秒前
深情安青应助木子李采纳,获得10
2秒前
左岸心诚完成签到,获得积分20
2秒前
2秒前
3秒前
科研通AI6.2应助研友_85YNe8采纳,获得10
3秒前
mingyahaoa完成签到,获得积分10
3秒前
4秒前
步步完成签到,获得积分10
4秒前
李健的小迷弟应助chenchen采纳,获得10
4秒前
4秒前
5秒前
斯文败类应助孤独静枫采纳,获得10
5秒前
咩eteor发布了新的文献求助10
7秒前
7秒前
丘丘发布了新的文献求助10
7秒前
风清扬发布了新的文献求助30
7秒前
MHR发布了新的文献求助50
9秒前
9秒前
吃的发布了新的文献求助10
9秒前
在水一方应助Felix采纳,获得10
9秒前
9秒前
10秒前
Hello应助xiaoxiaoliu采纳,获得10
11秒前
J明完成签到,获得积分10
11秒前
cgyaooo发布了新的文献求助10
11秒前
张静发布了新的文献求助10
11秒前
11秒前
Akim应助星无痕采纳,获得30
12秒前
晗晗有酒窝完成签到,获得积分10
13秒前
13秒前
13秒前
13秒前
腼腆的寒风完成签到 ,获得积分10
13秒前
14秒前
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Synthesis of P-Chiral Phosphine Ligands and Their Applications in Asymmetric Catalysis 400
Management and the Arts 310
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7629771
求助须知:如何正确求助?哪些是违规求助? 9204099
关于积分的说明 19737206
捐赠科研通 7199233
什么是DOI,文献DOI怎么找? 3274326
关于科研通互助平台的介绍 2436461
邀请新用户注册赠送积分活动 2270482